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Senior Postdoctoral Researcher in Computational Biology

University of Oxford - Oxford Vaccine Group, Centre for Clinical Vaccinology and Tropical Medicine / Institute of Developmental and Regenerative Medicine

Location: Oxford
Salary: £49,119 to £58,265 per annum (with a discretionary range to £65,336 per annum)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 9th October 2026
Closes: 21st October 2026
Job Ref: 189131

About the role 

We have an exciting opportunity for a highly motivated Senior Postdoctoral Research in Computational Biology to join the Oxford Vaccine Group (OVG). Working within a pioneering multidisciplinary research programme, you will contribute to the development of innovative computational and systems biology approaches to uncover the mechanisms underlying protection against infectious diseases and vaccine-induced immunity. By integrating complex datasets generated through vaccine studies and controlled human infection models (CHIMs), the programme aims to accelerate vaccine discovery through the application of systems immunology and artificial intelligence.

The postholder will lead a defined area of computational biology and bioinformatics research within the programme, developing original research questions and applying advanced computational approaches to analyse and integrate complex clinical, immunological and multi-omic datasets generated through controlled human infection, vaccination and related studies. They will develop and implement new computational and bioinformatic methodologies, including reproducible analytical workflows, statistical and predictive approaches, to investigate immune responses and biological mechanisms associated with protection against infection and vaccination.

Working closely with immunologists, clinicians, microbiologists, statisticians, data scientists and AI specialists, the postholder will provide computational leadership within multidisciplinary research projects, integrating computational and experimental findings to address the programme's scientific objectives. They will have substantial scope for scientific independence, including generating original research ideas, identifying new research opportunities arising from programme datasets and contributing to the development of the programme's computational biology research strategy. The postholder will also provide scientific guidance and supervision to junior researchers and students, contribute to collaborative funding applications, and lead and contribute substantially to the dissemination of research through high-quality peer-reviewed publications, seminars and presentations at scientific conferences. 

About you 

You will hold a PhD/DPhil in computational biology, bioinformatics, biological sciences, statistics, mathematics or a related discipline, with significant post-qualification research experience and the specialist expertise required to independently lead a defined area of computational biology research. You will have substantial experience applying computational and bioinformatic approaches to complex biological datasets, including high-throughput and/or multi-omic data, together with proficiency in relevant programming languages such as R and/or Python and experience developing reproducible computational research workflows. 

You will have a strong publication record and demonstrable experience developing original research questions and translating these into appropriate computational and analytical approaches. You will be able to independently plan and manage research activities and associated resources, contribute to the development of research strategy and funding applications, and provide scientific guidance and supervision to junior researchers. Excellent scientific communication and collaborative skills are essential, including the ability to work effectively with immunologists, clinicians, microbiologists, statisticians, data scientists and AI specialists and to represent the research group externally.

Experience of controlled human infection or vaccination studies, integration of single-cell and multi-omic datasets, machine learning or AI approaches, and HPC or cloud computing would be advantageous.

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